Study of Ecosystem Degradation Dynamics in the Peruvian Highlands: Landsat Time-Series Trend Analysis (1985–2022) with ARVI for Different Vegetation Cover Types

Deyvis Cano (), Samuel Pizarro, Carlos Cacciuttolo (), Richard Peñaloza, Raúl Yaranga and Marcelo Luciano Gandini
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Deyvis Cano: Programa Académico de Ingeniería Ambiental, Universidad de Huánuco, Huánuco 10003, Peru
Samuel Pizarro: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande-Hualahoyo Km 8 Santa Ana, Huancayo 12002, Peru
Carlos Cacciuttolo: Department of Civil Works and Geology, Catholic University of Temuco, Temuco 4780000, Chile
Richard Peñaloza: Environmental Science & Health—ESH Research Group, Facultad de Medicina Humana, Universidad Nacional del Centro del Perú, Av. Mariscal Castilla N° 3909, Huancayo 12006, Peru
Raúl Yaranga: Andean Ecosystem Research Group, Facultad de Zootecnia, Universidad Nacional del Centro del Perú, Av. Mariscal Castilla 3089, Huancayo, Junin 12002, Peru
Marcelo Luciano Gandini: Laboratorio de Investigación y Servicios en Teledetección, Facultad de Agronomía, NUCEVA, Universidad Nacional del Centro de la Provincia de Buenos Aires, Av. República de Italia 780, Azul 7300, Argentina

Sustainability, 2023, vol. 15, issue 21, 1-19

Abstract: The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation ( p < 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized.

Keywords: degradation; high-Andean vegetation; ARVI; Mann–Kendall; Landsat 5, 7 and 8; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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